Another fix approach

app.py

@@ -9,6 +9,29 @@ from fastai.callback.all import *
 from fastai.learner import *
 from fastai.optimizer import *
 from transformers import *
+import nltk
+
+nltk.download('punkt', quiet=True)
+
+pre_trained_model_name = "sshleifer/distilbart-cnn-6-6"
+hf_arch, hf_config, hf_tokenizer, hf_model = BlurrText().get_hf_objects(pre_trained_model_name, model_cls=BartForConditionalGeneration)
+summarization_metrics = {
+    "rouge": {
+        "compute_kwargs": {"rouge_types": ["rouge1", "rouge2", "rougeL", "rougeLsum"], "use_stemmer": True}, "returns": ["rouge1", "rouge2", "rougeL", "rougeLsum"],
+    },
+    "bertscore": {
+        "compute_kwargs": {"lang": "en"}, "returns": ["precision", "recall", "f1"]
+    }
+}
+translation_metrics = {
+    "bleu": {"returns": "bleu"},
+    "meteor": {"returns": "maeteor"},
+    "sacrebleu": {"returns": "score"},
+}
+
+model = BaseModelWrapper(hf_model)
+learn_cbs = [BaseModelCallback]
+fit_cbs = [Seq2SeqMetricsCallback(custom_metrics=summarization_metrics, calc_every="last_epoch")]
 
 learn = load_learner(fname='cnn_model.pkl')